mirror of
https://github.com/Sneed-Group/RobloxPotatoes
synced 2024-10-30 00:17:57 +00:00
83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
import tensorflow as tf
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import tensorflow_hub as hub
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import numpy as np
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import pyautogui
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import win32api, win32con, win32gui
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import cv2
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import math
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import time
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import os
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dirname = os.path.dirname(__file__)
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detector = tf.saved_model.load(dirname)
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size_scale = 3
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while True:
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# Get rect of Window
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hwnd = win32gui.FindWindow(None, 'Roblox')
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#hwnd = win32gui.FindWindow("UnrealWindow", None) # Fortnite
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rect = win32gui.GetWindowRect(hwnd)
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region = rect[0], rect[1], rect[2] - rect[0], rect[3] - rect[1]
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# Get image of screen
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ori_img = np.array(pyautogui.screenshot(region=region))
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ori_img = cv2.resize(ori_img, (ori_img.shape[1] // size_scale, ori_img.shape[0] // size_scale))
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image = np.expand_dims(ori_img, 0)
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img_w, img_h = image.shape[2], image.shape[1]
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# Detection
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result = detector(image)
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result = {key:value.numpy() for key,value in result.items()}
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boxes = result['detection_boxes'][0]
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scores = result['detection_scores'][0]
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classes = result['detection_classes'][0]
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# Check every detected object
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detected_boxes = []
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for i, box in enumerate(boxes):
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# Choose only person(class:1)
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if classes[i] == 1 and scores[i] >= 0.5:
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ymin, xmin, ymax, xmax = tuple(box)
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if ymin > 0.5 and ymax > 0.8: # CS:Go
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#if int(xmin * img_w * 3) < 450: # Fortnite
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continue
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left, right, top, bottom = int(xmin * img_w), int(xmax * img_w), int(ymin * img_h), int(ymax * img_h)
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detected_boxes.append((left, right, top, bottom))
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#cv2.rectangle(ori_img, (left, top), (right, bottom), (255, 255, 0), 2)
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print("Detected:", len(detected_boxes))
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# Check Closest
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if len(detected_boxes) >= 1:
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min = 99999
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at = 0
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centers = []
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for i, box in enumerate(detected_boxes):
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x1, x2, y1, y2 = box
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c_x = ((x2 - x1) / 2) + x1
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c_y = ((y2 - y1) / 2) + y1
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centers.append((c_x, c_y))
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dist = math.sqrt(math.pow(img_w/2 - c_x, 2) + math.pow(img_h/2 - c_y, 2))
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if dist < min:
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min = dist
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at = i
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# Pixel difference between crosshair(center) and the closest object
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x = centers[at][0] - img_w/2
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y = centers[at][1] - img_h/2 - (detected_boxes[at][3] - detected_boxes[at][2]) * 0.45
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# Move mouse and shoot
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scale = 1.7 * size_scale
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x = int(x * scale)
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y = int(y * scale)
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win32api.mouse_event(win32con.MOUSEEVENTF_MOVE, x, y, 0, 0)
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time.sleep(0.05)
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win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, x, y, 0, 0)
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time.sleep(0.1)
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win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, x, y, 0, 0)
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#ori_img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)
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#cv2.imshow("ori_img", ori_img)
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#cv2.waitKey(1)
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time.sleep(0.1)
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